Triple
T11884563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Morrison |
E282744
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Dope |
E237583
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Dope | Statement: [Rachel Morrison, notableWork, Dope]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dope Context triple: [Rachel Morrison, notableWork, Dope]
-
A.
Dope
"Dope" is a hip-hop single by rapper Legend, showcasing his style and lyrical approach within the genre.
-
B.
Dope
chosen
Dope is a 2015 coming-of-age comedy-drama film that follows a geeky teenager navigating life in a tough Los Angeles neighborhood after a chance encounter with the underground drug world.
-
C.
Dope
"Dope" is a song by Canadian singer-songwriter Jessie Reyez that showcases her raw, emotional vocal style and confessional lyricism.
-
D.
The Magnificent Dope
The Magnificent Dope is a 1942 American comedy film starring Henry Fonda and Lynn Bari that satirizes self-improvement fads and the pursuit of success.
-
E.
Dopesick
Dopesick is a drama miniseries that explores the origins and devastating impact of the U.S. opioid crisis, focusing on the roles of pharmaceutical companies, doctors, and law enforcement.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8d3a02ad4819090faef0e0be732ee |
completed | April 10, 2026, 10:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f281f488a8819082f40dfdd12f29a2 |
completed | April 29, 2026, 10:11 p.m. |
Created at: April 8, 2026, 9:44 p.m.